Resolving Molecular Perturbations Near Undercoordinated Metals

Metal surfaces can act as efficient heterogeneous catalysts, but their underlying mechanisms are often poorly understood. This is due to the highly transient nature of the underpinning interactions occurring at the single-molecule level, making these difficult to resolve by using traditional analysis techniques. Here, we present a methodology to study metal-molecule interactions near undercoordinated binding sites using single-molecule surface-enhanced Raman spectroscopy (SERS). We demonstrate how machine learning can identify the metal-induced molecular perturbations by recognizing concurrent frequency wandering in vibrational energies, and we compare these peak displacements to extensive DFT modeling to reveal what interactions are occurring. This allows us to resolve how molecules are deformed as they interact with binding sites on metal surfaces. The work provides rare insight into the dynamics and behavior of molecules at catalytically active interfaces and can aid in the rational design of heterogeneous catalysts.